Different change of direction tests assess different physical ability parameters: Principal component analysis of nine change of direction tests

Change of direction (CoD) ability is critical for the success of athletes in many sports. The purpose of this study was to perform a principal component analysis using 9 CoD tests in order to reveal possible subcomponents of CoD ability, which could aid practitioners in test selection. Male and female kinesiology students (n = 76) performed all CoD tests and a 30-m sprint test in a quasi-randomized, counterbalanced order. Three components for males and two components for females were extracted from principle component analysis (variance explained = 82.3 and 71.4%, respectively). It seems that the CoD test should be subdivided into at least two components: a) “pure CoD tests” (such as 505 test, T-test and 180° turn) and maneuverability tests (such as AFL run, Illinois test and Figure of Eight test). Considering that different factors seem to underlie CoD and maneuverability, our findings have important practical implications for training design. If hopping/jumping CoD is important for a given athlete, it should also be tested separately.

Incipient Chiller Fault Diagnosis Using an Optimized Least Squares Support Vector Machine With Gravitational Search Algorithm

Operational faults in centrifugal chillers will lead to high energy consumption, poor indoor thermal comfort, and low operational safety, and thus it is of significance to detect and diagnose the anomalies timely and effectively, especially for those at their incipient stages. The least squares support vector machine (LSSVM) has been regarded as an effective algorithm for multiclass classification. One of the most difficult issues in LSSVM is parameter tuning. Therefore, this paper reports a development of a gravitational search algorithm (GSA) optimized LSSVM method for incipient fault diagnosis in centrifugal chillers. Considering the inadequacies of conventional principle component analysis (PCA) algorithm for nonlinear data transformation, kernel principle component analysis (KPCA) was firstly employed to reduce the dimensionality of the original input data. Secondly, an optimized “one against one” multi-class LSSVM classifier was developed and its penalty constant and kernel bandwidth were tuned by GSA. Based on the fault samples of seven typical faults at their incipient stages in chillers from ASHRAE RP 1043, the proposed GSA optimized LSSVM fault diagnostic model was trained and validated. For the purpose of demonstrating the priority of the proposed fault diagnosis method, the obtained results were compared to that of using the LSSVM classifier optimized by another two algorithms, namely, the conventional cross-validation method and particle swarm optimizer. Results showed that the best fault diagnosis performance could be achieved using the proposed GSA-LSSVM classifier. The overall average fault diagnosis accuracy for the least severity faults was reported over 95%.

Brain tumor identification with a hybrid feature extraction method based on discrete wavelet transform and principle component analysis

The Identification of brain tumors is a critical step that relies on the expertise and abilities of the physician. In order to enable radiologists to spot brain tumors, an automated tumor arrangement is extremely important. This paper presents a technique for MR brain image segmentation and classification to identify images as normal and abnormal. The proposed technique is a hybrid feature extraction submitted to enhance the classification results and basically consists of three stages. The first stage is used a 3-level of discrete wavelet transform (DWT) to extract image characteristics. In the second stage, the principle component analysis (PCA) is applied to reduce the size of characteristics. Finally, a random forest classifier (RF) was used with a feature selection for identification. 181 MR brain images are collected (81 normal and 100 abnormal), in distinguishing normal and abnormal tissues, the experimental results obtained an accuracy of 98%, the sensitivity achieved is 99.2%, specificity achieved is 97.8%, and showed the effectiveness of the proposed technique compared with many kinds of literature. The results show that the 3L-DWT+PCA+RF still achieved the best classification results. The proposed model could apply to the brain MRI sphere classification, which will help doctors to diagnose a tumor if it is normal or abnormal in certain degrees.

Academic Resilience and Relationship with Meaning of Life Among Koya University Students

This study aimed to find out the level of academic resilience (AR), meaning of life (MOL) and the relationship between them among students at Koya University. 740 samples were selected randomly. The researcher used two scales; one scale adapted academic resilience scale by Cassidy (2016), and meaning of life scale was developed by the researchers. Principle Component Analysis (PCA) was conducted to test the validity of the meaning of life items. The validity and reliability of the instruments were at convinced level. The result showed that the students have a low level of academic resilience and a high level of meaning of life; the result showed a statistically positive relationship between academic resilience and meaning of life, also the result showed that the academic resilience was predicted meaning of life.

Principle Component Analysis for Crop Discrimination using Hyperspectral Remote Sensing Data

Crop discrimination is still very challenging issue for researcher because of spectral reflectance similarity captured in non-imaging data. The objective of this research work is to focus on crop discrimination challenge. We have used ASD FieldSpec4 Spectroradiometer for collection of leaf samples of four crops Wheat, Jowar, Bajara and Maize. We used vegetation indices and some spectral reflectance band for featuring our dataset. We applied Principle Component Analysis (PCA) for discrimination and it has been observed that when we use first and second principle component, it will give poor result but if third principle component is used then we get accurate and fine results.